Robust Short-Time Fourier Transform acoustic echo cancellation during audio playback

ABSTRACT

Example techniques involve noise-robust acoustic echo cancellation. An example implementation may involve causing one or more speakers of the playback device to play back audio content and while the audio content is playing back, capturing, via the one or more microphones, audio within an acoustic environment that includes the audio playback. The example implementation may involve determining measured and reference signals in the STFT domain. During each n th  iteration of an acoustic echo canceller (AEC): the implementation may involve determining a frame of an output signal by generating a frame of a model signal by passing a frame of the reference signal through an instance of an adaptive filter and then redacting the n th  frame of the model signal from an n th  frame of the measured signal. The implementation may further involve determining an instance of the adaptive filter for a next iteration of the AEC.

CROSS REFERENCE TO RELATED APPLICATIONS

This application claims priority under 35 U.S.C. § 120 to, and is acontinuation of, U.S. non-provisional patent application Ser. No.15/717,621, filed on Sep. 27, 2017, entitled “Robust Short-Time FourierTransform Acoustic Echo Cancellation During Audio Playback,” which isincorporated herein by reference in its entirety.

FIELD OF THE DISCLOSURE

The disclosure is related to consumer goods and, more particularly, tomethods, systems, products, features, services, and other elementsdirected to media playback or some aspect thereof.

BACKGROUND

Options for accessing and listening to digital audio in an out-loudsetting were limited until in 2003, when SONOS, Inc. filed for one ofits first patent applications, entitled “Method for Synchronizing AudioPlayback between Multiple Networked Devices,” and began offering a mediaplayback system for sale in 2005. The Sonos Wireless HiFi System enablespeople to experience music from many sources via one or more networkedplayback devices. Through a software control application installed on asmartphone, tablet, or computer, one can play what he or she wants inany room that has a networked playback device. Additionally, using thecontroller, for example, different songs can be streamed to each roomwith a playback device, rooms can be grouped together for synchronousplayback, or the same song can be heard in all rooms synchronously.

Given the ever-growing interest in digital media, there continues to bea need to develop consumer-accessible technologies to further enhancethe listening experience.

BRIEF DESCRIPTION OF THE DRAWINGS

Features, aspects, and advantages of the presently disclosed technologymay be better understood with regard to the following description,appended claims, and accompanying drawings where:

FIG. 1 shows a media playback system configuration in which certainembodiments may be practiced;

FIG. 2 is a functional block diagram of an example playback device;

FIG. 3 is a functional block diagram of an example controller device;

FIGS. 4A and 4B are controller interfaces;

FIG. 5A is a functional block diagram of an example network microphonedevice in accordance with aspects of the disclosure;

FIG. 5B is a diagram of an example voice input in accordance withaspects of the disclosure;

FIG. 6 is a functional block diagram of example remote computingdevice(s) in accordance with aspects of the disclosure;

FIG. 7 is a schematic diagram of an example network system in accordancewith aspects of the disclosure;

FIG. 8A is a functional block diagram of an example acoustic echocancellation pipeline;

FIG. 8B is a functional block diagram of an example acoustic echocancellation pipeline;

FIG. 9 is a flow diagram of a method of performing acoustic echocancellation.

The drawings are for purposes of illustrating example embodiments, butit is understood that the inventions are not limited to the arrangementsand instrumentality shown in the drawings. In the drawings, identicalreference numbers identify at least generally similar elements. Tofacilitate the discussion of any particular element, the mostsignificant digit or digits of any reference number refers to the Figurein which that element is first introduced. For example, element 110 isfirst introduced and discussed with reference to FIG. 1.

DETAILED DESCRIPTION I. Overview

Networked microphone devices may be used to control a household usingvoice control. Voice control can be beneficial for a “smart” home havinga system of smart devices, such as playback devices, wirelessillumination devices, thermostats, door locks, home-automation devices,as well as other examples. In some implementations, the system of smartdevices includes a networked microphone device configured to detectvoice inputs. A voice assistant service facilitates processing of thevoice inputs. Traditionally, the voice assistant service includes remoteservers that receive and process voice inputs. The voice service mayreturn responses to voice inputs, which might include control of varioussmart devices or audio or video information (e.g., a weather report),among other examples.

A voice input typically includes an utterance with a wake word followedby an utterance containing a user request. A wake word, when uttered,may invoke a particular voice assistance service. For instance, inquerying the AMAZON® voice assistant service, a user might speak a wakeword “Alexa.” Other examples include “Ok, Google” for invoking theGOOGLE® voice assistant service and “Hey, Siri” for invoking the APPLE®voice assistant service.

Upon detecting a wake word, a networked microphone device may listen forthe user request in the voice utterance following the wake word. In someinstances, the user request may include a command to control a thirdparty device, such as a smart illumination device (e.g., a PHILIPS HUE®lighting device), a thermostat (e.g., NEST® thermostat), or a mediaplayback device (e.g., a Sonos® playback device). For example, a usermight speak the wake word “Alexa” followed by the utterance “turn on theliving room” to turn on illumination devices. A user might speak thesame wake word followed by the utterance “set the thermostat to 68degrees.” The user may also utter a request for a playback device toplay a particular song, an album, or a playlist of music.

When a playback device is playing audio in the same acoustic environmentas a networked microphone device, sound captured by the microphone(s) ofthe networked microphone device might include the sound of the audioplayback as well as an uttered voice input. Since the sound of the audioplayback might interfere with processing of the voice input by a voiceassistant service (e.g., if the audio playback drowns out the voiceinput), an Acoustic Echo Canceller (“AEC”) may be used to remove thesound of the audio playback from the signal captured by microphone(s) ofthe networked microphone device. This removal is intended to improve thesignal-to-noise ratio of a voice input to other sound within theacoustic environment, which includes the sound produced by the one ormore speakers in playing back the audio content, so as to provide a lessnoisy signal to the voice assistant service.

In example implementations, an AEC is implemented within the audioprocessing pipeline of an audio playback device or a networkedmicrophone device. Inputs to an AEC may include the signal captured bythe microphone(s) of a networked microphone device, and a referencesignal. To represent the audio playback as closely as practical, thereference signal may be taken from a point in the audio playbackpipeline that closely represents the analog audio expected to be outputby the transducers. Given these inputs, the AEC attempts to find atransfer function (i.e., a ‘filter’) that transforms the referencesignal into the captured microphone signal with minimal error. Invertingthe resulting AEC output and mixing it with the microphone signal causesa redaction of the audio output signal from the signal captured by themicrophone(s).

As those of ordinary skill in the art will appreciate, one issue withconventional AEC techniques is ‘double-talk’. Double-talk can occur, forexample, when two people talk concurrently in the same acousticenvironment being captured by the microphones. A conventional AEC maytreat one ‘voice’ as an input while the other voice is treated aschanging room effect. In this condition, the conventional AEC mayattempt to adapt to the changing “room effect” but cannot keep up withthe pace of advancement of the speech. In such conditions, the AEC mayde-stabilize and introduce more noise into the system than it wassupposed to remove. Yet, the capture of multiple concurrent voices isexpected to be a common condition in many environments, such as a homewith multiple users and possibly multiple networked microphone devices.

To avoid this condition, some systems have implemented a double-talkdetector, which is designed to detect when two or more users are talkingin the same acoustic environment and suspending the AEC during thedouble-talk condition. Using a double-talk detector may help to avoiddestabilization of the AEC during double-talk conditions. But bysuspending the AEC during the double-talk condition, the AEC no longercancels echoes within the acoustic environment, which ultimately resultsin a “noisier” voice input to the voice assistant service. Moreover,utilizing a double-talk detector requires additional processingcapability.

Example implementations described herein may improve acoustic echocancellation though a combination of techniques. Such techniques mayinclude processing in the Short-Time Fourier Transform (“STFT”) insteadof the Frequency-Dependent Adaptive Filter (“FDAF”) domain). Thetechniques may also include using a mathematical processing model thatkeeps the AEC robust in face of double-talk conditions and in noisyenvironments. The techniques can further include applying a sparsitycriterion that improves converge rate of the adaptive filter by focusingadaptation of the filter on only those areas of the impulse responsewhich are in greatest error. Inactive portions of the filter aredeactivated, so as to allow use of a high order multi delay filter whereonly the partitions that correspond to the actual model are active,thereby increasing stability and hastening convergence.

These techniques can result in tolerance for frequent double-talkconditions without compromising AEC performance during audio playback.

Example techniques described herein may involve acoustic echocancellation. An example implementation may involve causing, via anaudio stage, the one or more speakers to play back audio content andwhile the audio content is playing back via the one or more speakers,capturing, via the one or more microphones, audio within an acousticenvironment, wherein the captured audio comprises audio signalsrepresenting sound produced by the one or more speakers in playing backthe audio content. The example implementation may further involvereceiving an output signal from the audio stage representing the audiocontent being played back by the one or more speakers, determining ameasured signal comprising a series of frames representing the capturedaudio within the acoustic environment by transforming into a short timeFourier transform (STFT) domain the captured audio within the acousticenvironment, and determining a reference signal comprising a series offrames representing the audio content being played back via the one ormore speakers by transforming into the STFT domain the received outputsignal from the audio stage.

During each n^(th) iteration of an acoustic echo canceller (AEC), theimplementation may involve determining an n^(th) frame of an outputsignal. Determining the n^(th) frame of the output signal may involvegenerating an n^(th) frame of a model signal by passing an n^(th) frameof the reference signal through an n^(th) instance of an adaptivefilter, wherein the first instance of the adaptive filter is an initialfilter; and generating the n^(th) frame of the output signal byredacting the n^(th) frame of the model signal from an n^(th) frame ofthe measured signal. The example implementation may also involve sendingthe output signal as a voice input to one or more voice services forprocessing of the voice input

The implementation may further involve, during each n^(th) iteration ofthe acoustic echo canceller (AEC), determining a n+1^(th) instance ofthe adaptive filter for a next iteration of the AEC. Determining then+1^(th) instance of the adaptive filter for the next iteration of theAEC may involve determining an n^(th) frame of an error signal, then^(th) frame of the error signal representing a difference between then^(th) frame of the model signal and the n^(th) frame of the referencesignal less audio signals representing sound from sources other than ann^(th) frame of the audio signals representing sound produced by the oneor more speakers in playing back the n^(th) frame of the referencesignal, determining a normalized least mean square (NMLS) of the n^(th)frame of the error signal, determining a sparse NMLS of the n^(th) frameof the error signal by applying to the NMLS of the n^(th) frame of theerror signal, a sparse partition criterion that zeroes out frequencybands of the NMLS having less than a threshold energy, converting thesparse NMLS of the n^(th) frame of the error signal to an n^(th) updatefilter, and generating the n+1^(th) instance of the adaptive filter forthe next iteration of the AEC by summing the n^(th) instance of theadaptive filter with the n^(th) update filter.

This example implementation may be embodied as a method, a deviceconfigured to carry out the implementation, a system of devicesconfigured to carry out the implementation, or a non-transitorycomputer-readable medium containing instructions that are executable byone or more processors to carry out the implementation, among otherexamples. It will be understood by one of ordinary skill in the art thatthis disclosure includes numerous other embodiments, includingcombinations of the example features described herein. Further, anyexample operation described as being performed by a given device toillustrate a technique may be performed by any suitable devices,including the devices described herein. Yet further, any device maycause another device to perform any of the operations described herein.

While some examples described herein may refer to functions performed bygiven actors such as “users” and/or other entities, it should beunderstood that this description is for purposes of explanation only.The claims should not be interpreted to require action by any suchexample actor unless explicitly required by the language of the claimsthemselves.

II. Example Operating Environment

FIG. 1 illustrates an example configuration of a media playback system100 in which one or more embodiments disclosed herein may beimplemented. The media playback system 100 as shown is associated withan example home environment having several rooms and spaces, such as forexample, an office, a dining room, and a living room. Within these roomsand spaces, the media playback system 100 includes playback devices 102(identified individually as playback devices 102 a-1021), networkmicrophone devices 103 (identified individually as “NMD(s)” 103 a-103g), and controller devices 104 a and 104 b (collectively “controllerdevices 104”). The home environment may include other network devices,such as one or more smart illumination devices 108 and a smartthermostat 110.

The various playback, network microphone, and controller devices 102-104and/or other network devices of the media playback system 100 may becoupled to one another via point-to-point and/or over other connections,which may be wired and/or wireless, via a local area network (LAN) via anetwork router 106. For example, the playback device 102 j (designatedas “LEFT”) may have a point-to-point connection with the playback device102 a (designated as “RIGHT”). In one embodiment, the LEFT playbackdevice 102 j may communicate over the point-to-point connection with theRIGHT playback device 102 a. In a related embodiment, the LEFT playbackdevice 102 j may communicate with other network devices via thepoint-to-point connection and/or other connections via the LAN.

The network router 106 may be coupled to one or more remote computingdevice(s) 105 via a wide area network (WAN) 107. In some embodiments,the remote computing device(s) may be cloud servers. The remotecomputing device(s) 105 may be configured to interact with the mediaplayback system 100 in various ways. For example, the remote computingdevice(s) may be configured to facilitate streaming and controllingplayback of media content, such as audio, in the home environment. Inone aspect of the technology described in greater detail below, theremote computing device(s) 105 are configured to provide an enhanced VAS160 for the media playback system 100.

In some embodiments, one or more of the playback devices 102 may includean onboard (e.g., integrated) network microphone device. For example,the playback devices 102 a-e include corresponding NMDs 103 a-e,respectively. Playback devices that include network devices may bereferred to herein interchangeably as a playback device or a networkmicrophone device unless expressly stated otherwise.

In some embodiments, one or more of the NMDs 103 may be a stand-alonedevice. For example, the NMDs 103 f and 103 g may be stand-alone networkmicrophone devices. A stand-alone network microphone device may omitcomponents typically included in a playback device, such as a speaker orrelated electronics. In such cases, a stand-alone network microphonedevice might not produce audio output or may produce limited audiooutput (e.g., relatively low-quality output relative to quality ofoutput by a playback device).

In some embodiments, one or more network microphone devices can beassigned to a playback device or a group of playback devices. In someembodiments, a network microphone device can be assigned to a playbackdevice that does not include an onboard network microphone device. Forexample, the NMD 103 f may be assigned to one or more of the playbackdevices 102 in its vicinity, such as one or both of the playback devices102 i and 102 l in the kitchen and dining room spaces, respectively. Insuch a case, the NMD 103 f may output audio through the playbackdevice(s) to which it is assigned. Further details regarding assignmentof network microphone devices are described, for example, in U.S.application Ser. No. 15/098,867 filed on Apr. 14, 2016, and titled“Default Playback Device Designation,” and U.S. application Ser. No.15/098,892 filed on Apr. 14, 2016 and titled “Default Playback Devices.”Each of these applications is incorporated herein by reference in itsentirety.

In some embodiments, a network microphone device may be configured suchthat it is dedicated exclusively to a particular VAS. In one example,the NMD 103 a in the living room space may be dedicated exclusively tothe enhanced VAS 160. In such case, the NMD 102 a might not invoke anyother VAS except the enhanced VAS 160. In a related example, other onesof the NMDs 103 may be configured to invoke the enhanced 160 VAS and oneor more other VASes, such as a traditional VAS. Other examples ofbonding and assigning network microphone devices to playback devicesand/or VASes are possible. In some embodiments, the NMDs 103 might notbe bonded or assigned in a particular manner.

Further aspects relating to the different components of the examplemedia playback system 100 and how the different components may interactto provide a user with a media experience may be found in the followingsections. While discussions herein may generally refer to the examplemedia playback system 100, technologies described herein are not limitedto applications within, among other things, the home environment asshown in FIG. 1. For instance, the technologies described herein may beuseful in other home environment configurations comprising more or fewerof any of the playback, network microphone, and/or controller devices102-104. Additionally, the technologies described herein may be usefulin environments where multi-zone audio may be desired, such as, forexample, a commercial setting like a restaurant, mall or airport, avehicle like a sports utility vehicle (SUV), bus or car, a ship or boat,an airplane, and so on.

a. Example Playback Devices

FIG. 2 is a functional block diagram illustrating certain aspects of aselected one of the playback devices 102 shown in FIG. 1. As shown, sucha playback device may include a processor 212, software components 214,memory 216, audio processing components 218, audio amplifier(s) 220,speaker(s) 222, and a network interface 230 including wirelessinterface(s) 232 and wired interface(s) 234. In some embodiments, aplayback device might not include the speaker(s) 222, but rather aspeaker interface for connecting the playback device to externalspeakers. In certain embodiments, the playback device includes neitherthe speaker(s) 222 nor the audio amplifier(s) 222, but rather an audiointerface for connecting a playback device to an external audioamplifier or audio-visual receiver.

A playback device may further include a user interface 236. The userinterface 236 may facilitate user interactions independent of or inconjunction with one or more of the controller devices 104. In variousembodiments, the user interface 236 includes one or more of physicalbuttons and/or graphical interfaces provided on touch sensitivescreen(s) and/or surface(s), among other possibilities, for a user todirectly provide input. The user interface 236 may further include oneor more of lights and the speaker(s) to provide visual and/or audiofeedback to a user.

In some embodiments, the processor 212 may be a clock-driven computingcomponent configured to process input data according to instructionsstored in the memory 216. The memory 216 may be a tangiblecomputer-readable medium configured to store instructions executable bythe processor 212. For example, the memory 216 may be data storage thatcan be loaded with one or more of the software components 214 executableby the processor 212 to achieve certain functions. In one example, thefunctions may involve a playback device retrieving audio data from anaudio source or another playback device. In another example, thefunctions may involve a playback device sending audio data to anotherdevice on a network. In yet another example, the functions may involvepairing of a playback device with one or more other playback devices tocreate a multi-channel audio environment.

Certain functions may involve a playback device synchronizing playbackof audio content with one or more other playback devices. Duringsynchronous playback, a listener should not perceive time-delaydifferences between playback of the audio content by the synchronizedplayback devices. U.S. Pat. No. 8,234,395 filed Apr. 4, 2004, and titled“System and method for synchronizing operations among a plurality ofindependently clocked digital data processing devices,” which is herebyincorporated by reference in its entirety, provides in more detail someexamples for audio playback synchronization among playback devices.

The memory 216 may be further configured to store data associated with aplayback device. For example, the memory may store data corresponding toone or more zones and/or zone groups a playback device is a part of. Oneor more of the zones and/or zone groups may be named according to theroom or space in which device(s) are located. For example, the playbackand network microphone devices in the living room space shown in FIG. 1may be referred to as a zone group named Living Room. As anotherexample, the playback device 102 l in the dining room space may be namedas a zone “Dining Room.” The zones and/or zone groups may also haveuniquely assigned names, such as “Nick's Room,” as shown in FIG. 1.

The memory 216 may be further configured to store other data. Such datamay pertain to audio sources accessible by a playback device or aplayback queue that the playback device (or some other playbackdevice(s)) may be associated with. The data stored in the memory 216 maybe stored as one or more state variables that are periodically updatedand used to describe the state of the playback device. The memory 216may also include the data associated with the state of the other devicesof the media system, and shared from time to time among the devices sothat one or more of the devices have the most recent data associatedwith the system. Other embodiments are also possible.

The audio processing components 218 may include one or moredigital-to-analog converters (DAC), an audio preprocessing component, anaudio enhancement component or a digital signal processor (DSP), and soon. In some embodiments, one or more of the audio processing components218 may be a subcomponent of the processor 212. In one example, audiocontent may be processed and/or intentionally altered by the audioprocessing components 218 to produce audio signals. The produced audiosignals may then be provided to the audio amplifier(s) 210 foramplification and playback through speaker(s) 212. Particularly, theaudio amplifier(s) 210 may include devices configured to amplify audiosignals to a level for driving one or more of the speakers 212. Thespeaker(s) 212 may include an individual transducer (e.g., a “driver”)or a complete speaker system involving an enclosure with one or moredrivers. A particular driver of the speaker(s) 212 may include, forexample, a subwoofer (e.g., for low frequencies), a mid-range driver(e.g., for middle frequencies), and/or a tweeter (e.g., for highfrequencies). In some cases, each transducer in the one or more speakers212 may be driven by an individual corresponding audio amplifier of theaudio amplifier(s) 210. In addition to producing analog signals forplayback, the audio processing components 208 may be configured toprocess audio content to be sent to one or more other playback devicesfor playback.

Audio content to be processed and/or played back by a playback devicemay be received from an external source, such as via an audio line-ininput connection (e.g., an auto-detecting 3.5 mm audio line-inconnection) or the network interface 230.

The network interface 230 may be configured to facilitate a data flowbetween a playback device and one or more other devices on a datanetwork. As such, a playback device may be configured to receive audiocontent over the data network from one or more other playback devices incommunication with a playback device, network devices within a localarea network, or audio content sources over a wide area network such asthe Internet. In one example, the audio content and other signalstransmitted and received by a playback device may be transmitted in theform of digital packet data containing an Internet Protocol (IP)-basedsource address and IP-based destination addresses. In such a case, thenetwork interface 230 may be configured to parse the digital packet datasuch that the data destined for a playback device is properly receivedand processed by the playback device.

As shown, the network interface 230 may include wireless interface(s)232 and wired interface(s) 234. The wireless interface(s) 232 mayprovide network interface functions for a playback device to wirelesslycommunicate with other devices (e.g., other playback device(s),speaker(s), receiver(s), network device(s), control device(s) within adata network the playback device is associated with) in accordance witha communication protocol (e.g., any wireless standard including IEEE802.11a, 802.11b, 802.11g, 802.11n, 802.11ac, 802.15, 4G mobilecommunication standard, and so on). The wired interface(s) 234 mayprovide network interface functions for a playback device to communicateover a wired connection with other devices in accordance with acommunication protocol (e.g., IEEE 802.3). While the network interface230 shown in FIG. 2 includes both wireless interface(s) 232 and wiredinterface(s) 234, the network interface 230 may in some embodimentsinclude only wireless interface(s) or only wired interface(s).

In some embodiments, a playback device and one other playback device maybe paired to play two separate audio components of audio content. Forexample, the LEFT playback device 102 j in the Living Room may beconfigured to play a left channel audio component, while the RIGHTplayback device 102 a may be configured to play a right channel audiocomponent, thereby producing or enhancing a stereo effect of the audiocontent. Similarly, the playback device 102 l designated to the DiningRoom may be configured to play a left channel audio component, while theplayback device 102 i designated to the Kitchen may be configured toplay a right channel audio component. Paired playback devices mayfurther play audio content in synchrony with other playback devices.Paired playback device may also be referred to as “bonded playbackdevices.

In some embodiments, one or more of the playback devices may besonically consolidated with one or more other playback devices to form asingle, consolidated playback device. A consolidated playback device mayinclude separate playback devices each having additional or differentspeaker drivers through which audio content may be rendered. Forexample, a playback device designed to render low frequency range audiocontent (e.g., the playback device 102 k designated as a subwoofer or“SUB”) may be consolidated with a full-frequency playback device (e.g.,the playback device 102 b designated as “FRONT”) to render the lowerfrequency range of the consolidated device. In such a case, the fullfrequency playback device, when consolidated with the low frequencyplayback device, may be configured to render only the mid and highfrequency components of audio content, while the low-frequency playbackdevice renders the low frequency component of the audio content. Theconsolidated playback device may be paired or consolidated with one ormore other playback devices. For example, FIG. 1 shows the SUB playbackdevice 102 k consolidated with the FRONT playback device 102 b to formsubwoofer and center channels, and further consolidated with the RIGHTplayback device 102 a and the LEFT playback device 102 j.

As discussed above, a playback device may include a network microphonedevice, such as one of the NMDs 103, as show in FIG. 2. A networkmicrophone device may share some or all the components of a playbackdevice, such as the processor 212, the memory 216, the microphone(s)224, etc. In other examples, a network microphone device includescomponents that are dedicated exclusively to operational aspects of thenetwork microphone device. For example, a network microphone device mayinclude far-field microphones and/or voice processing components, whichin some instances a playback device may not include. In another example,a network microphone device may include a touch-sensitive button forenabling/disabling a microphone. In yet another example, a networkmicrophone device can be a stand-alone device, as discussed above.

By way of illustration, SONOS, Inc. presently offers (or has offered)for sale certain playback devices including a “PLAY:1,” “PLAY:3,”“PLAY:5,” “PLAYBAR,” “CONNECT:AMP,” “CONNECT,” and “SUB.” Any otherpast, present, and/or future playback devices may additionally oralternatively be used to implement the playback devices of exampleembodiments disclosed herein. Additionally, it is understood that aplayback device is not limited to the example illustrated in FIG. 2 orto the SONOS product offerings. For example, a playback device mayinclude a wired or wireless headphone. In another example, a playbackdevice may include or interact with a docking station for personalmobile media playback devices. In yet another example, a playback devicemay be integral to another device or component such as a television, alighting fixture, or some other device for indoor or outdoor use.

b. Example Playback Zone Configurations

Referring back to the media playback system 100 of FIG. 1, the mediaplayback system 100 may be established with one or more playback zones,after which one or more of the playback and/or network devices 102-103may be added or removed to arrive at the example configuration shown inFIG. 1. As discussed above, zones and zone groups may be given a uniquename and/or a name corresponding to the space in which device(s) arelocated.

In one example, one or more playback zones in the environment of FIG. 1may each be playing different audio content. For instance, the user maybe grilling in the Balcony zone and listening to hip hop music beingplayed by the playback device 102 c while another user is preparing foodin the Kitchen zone and listening to classical music being played by theplayback device 102 i. In another example, a playback zone may play thesame audio content in synchrony with another playback zone. Forinstance, the user may be in the Office zone where the playback device102 d is playing the same hip-hop music that is being playing byplayback device 102 c in the Balcony zone. In such a case, playbackdevices 102 c and 102 d may be playing the hip-hop in synchrony suchthat the user may seamlessly (or at least substantially seamlessly)enjoy the audio content that is being played out-loud while movingbetween different playback zones. Synchronization among playback zonesmay be achieved in a manner similar to that of synchronization amongplayback devices, as described in previously referenced U.S. Pat. No.8,234,395.

A network microphone device may receive voice inputs from a user in itsvicinity. A network microphone device may capture a voice input upondetection of the user speaking the input. For instance, in the exampleshown in FIG. 1, the NMD 103 a may capture the voice input of a user inthe vicinity of the Living Room, Dining Room, and/or Kitchen zones. Insome instances, other network microphone devices in the homeenvironment, such as the NMD 104 f in the Kitchen and/or the other NMD104 b in the Living Room may capture the same voice input. In suchinstances, network devices that detect the voice input may be configuredto arbitrate between one another so that fewer or only the mostproximate one of the NMDs 103 process the user's voice input. Otherexamples for selecting network microphone devices for processing voiceinput can be found, for example, in U.S. patent application Ser. No.15/171,180 fled Jun. 9, 2016, and titled “Dynamic Player Selection forAudio Signal Processing” and U.S. patent application Ser. No. 15/211,748filed Jul. 15, 2016, and titled “Voice Detection by Multiple Devices.”Each of these references is incorporated herein by reference in itsentirety. A network microphone device may control selected playbackand/or network microphone devices 102-103 in response to voice inputs,as described in greater detail below.

As suggested above, the zone configurations of the media playback system100 may be dynamically modified. As such, the media playback system 100may support numerous configurations. For example, if a user physicallymoves one or more playback devices to or from a zone, the media playbacksystem 100 may be reconfigured to accommodate the change(s). Forinstance, if the user physically moves the playback device 102 c fromthe Balcony zone to the Office zone, the Office zone may now includeboth the playback devices 102 c and 102 d. In some cases, the use maypair or group the moved playback device 102 c with the Office zoneand/or rename the players in the Office zone using, e.g., one of thecontroller devices 104 and/or voice input. As another example, if one ormore playback devices 102 are moved to a particular area in the homeenvironment that is not already a playback zone, the moved playbackdevice(s) may be renamed or associated with a playback zone for theparticular area.

Further, different playback zones of the media playback system 100 maybe dynamically combined into zone groups or split up into individualplayback zones. For example, the Dining Room zone and the Kitchen zonemay be combined into a zone group for a dinner party such that playbackdevices 102 i and 102 l may render audio content in synchrony. Asanother example, playback devices 102 consolidated in the Living Roomzone for the previously described consolidated TV arrangement may besplit into (i) a television zone and (ii) a separate listening zone. Thetelevision zone may include the FRONT playback device 102 b. Thelistening zone may include the RIGHT, LEFT, and SUB playback devices 102a, 102 j, and 102 k, which may be grouped, paired, or consolidated, asdescribed above. Splitting the Living Room zone in such a manner mayallow one user to listen to music in the listening zone in one area ofthe living room space, and another user to watch the television inanother area of the living room space. In a related example, a user mayimplement either of the NMD 103 a or 103 b to control the Living Roomzone before it is separated into the television zone and the listeningzone. Once separated, the listening zone may be controlled by a user inthe vicinity of the NMD 103 a, and the television zone may be controlledby a user in the vicinity of the NMD 103 b. As described above, however,any of the NMDs 103 may be configured to control the various playbackand other devices of the media playback system 100.

c. Example Controller Devices

FIG. 3 is a functional block diagram illustrating certain aspects of aselected one of the controller devices 104 of the media playback system100 of FIG. 1. Such controller devices may also be referred to as acontroller. The controller device shown in FIG. 3 may include componentsthat are generally similar to certain components of the network devicesdescribed above, such as a processor 312, memory 316, microphone(s) 324,and a network interface 330. In one example, a controller device may bea dedicated controller for the media playback system 100. In anotherexample, a controller device may be a network device on which mediaplayback system controller application software may be installed, suchas for example, an iPhone™ iPad™ or any other smart phone, tablet ornetwork device (e.g., a networked computer such as a PC or Mac™).

The memory 316 of a controller device may be configured to storecontroller application software and other data associated with the mediaplayback system 100 and a user of the system 100. The memory 316 may beloaded with one or more software components 314 executable by theprocessor 312 to achieve certain functions, such as facilitating useraccess, control, and configuration of the media playback system 100. Acontroller device communicates with other network devices over thenetwork interface 330, such as a wireless interface, as described above.

In one example, data and information (e.g., such as a state variable)may be communicated between a controller device and other devices viathe network interface 330. For instance, playback zone and zone groupconfigurations in the media playback system 100 may be received by acontroller device from a playback device, a network microphone device,or another network device, or transmitted by the controller device toanother playback device or network device via the network interface 306.In some cases, the other network device may be another controllerdevice.

Playback device control commands such as volume control and audioplayback control may also be communicated from a controller device to aplayback device via the network interface 330. As suggested above,changes to configurations of the media playback system 100 may also beperformed by a user using the controller device. The configurationchanges may include adding/removing one or more playback devices to/froma zone, adding/removing one or more zones to/from a zone group, forminga bonded or consolidated player, separating one or more playback devicesfrom a bonded or consolidated player, among others.

The user interface(s) 340 of a controller device may be configured tofacilitate user access and control of the media playback system 100, byproviding controller interface(s) such as the controller interfaces 400a and 400 b (collectively “controller interface 440”) shown in FIGS. 4Aand 4B, respectively. Referring to FIGS. 4A and 4B together, thecontroller interface 440 includes a playback control region 442, aplayback zone region 443, a playback status region 444, a playback queueregion 446, and a sources region 448. The user interface 400 as shown isjust one example of a user interface that may be provided on a networkdevice such as the controller device shown in FIG. 3 and accessed byusers to control a media playback system such as the media playbacksystem 100. Other user interfaces of varying formats, styles, andinteractive sequences may alternatively be implemented on one or morenetwork devices to provide comparable control access to a media playbacksystem.

The playback control region 442 (FIG. 4A) may include selectable (e.g.,by way of touch or by using a cursor) icons to cause playback devices ina selected playback zone or zone group to play or pause, fast forward,rewind, skip to next, skip to previous, enter/exit shuffle mode,enter/exit repeat mode, enter/exit cross fade mode. The playback controlregion 442 may also include selectable icons to modify equalizationsettings, and playback volume, among other possibilities.

The playback zone region 443 (FIG. 4B) may include representations ofplayback zones within the media playback system 100. In someembodiments, the graphical representations of playback zones may beselectable to bring up additional selectable icons to manage orconfigure the playback zones in the media playback system, such as acreation of bonded zones, creation of zone groups, separation of zonegroups, and renaming of zone groups, among other possibilities.

For example, as shown, a “group” icon may be provided within each of thegraphical representations of playback zones. The “group” icon providedwithin a graphical representation of a particular zone may be selectableto bring up options to select one or more other zones in the mediaplayback system to be grouped with the particular zone. Once grouped,playback devices in the zones that have been grouped with the particularzone will be configured to play audio content in synchrony with theplayback device(s) in the particular zone. Analogously, a “group” iconmay be provided within a graphical representation of a zone group. Inthis case, the “group” icon may be selectable to bring up options todeselect one or more zones in the zone group to be removed from the zonegroup. Other interactions and implementations for grouping andungrouping zones via a user interface such as the user interface 400 arealso possible. The representations of playback zones in the playbackzone region 443 (FIG. 4B) may be dynamically updated as playback zone orzone group configurations are modified.

The playback status region 444 (FIG. 4A) may include graphicalrepresentations of audio content that is presently being played,previously played, or scheduled to play next in the selected playbackzone or zone group. The selected playback zone or zone group may bevisually distinguished on the user interface, such as within theplayback zone region 443 and/or the playback status region 444. Thegraphical representations may include track title, artist name, albumname, album year, track length, and other relevant information that maybe useful for the user to know when controlling the media playbacksystem via the user interface 440.

The playback queue region 446 may include graphical representations ofaudio content in a playback queue associated with the selected playbackzone or zone group. In some embodiments, each playback zone or zonegroup may be associated with a playback queue containing informationcorresponding to zero or more audio items for playback by the playbackzone or zone group. For instance, each audio item in the playback queuemay comprise a uniform resource identifier (URI), a uniform resourcelocator (URL) or some other identifier that may be used by a playbackdevice in the playback zone or zone group to find and/or retrieve theaudio item from a local audio content source or a networked audiocontent source, possibly for playback by the playback device.

In one example, a playlist may be added to a playback queue, in whichcase information corresponding to each audio item in the playlist may beadded to the playback queue. In another example, audio items in aplayback queue may be saved as a playlist. In a further example, aplayback queue may be empty, or populated but “not in use” when theplayback zone or zone group is playing continuously streaming audiocontent, such as Internet radio that may continue to play untilotherwise stopped, rather than discrete audio items that have playbackdurations. In an alternative embodiment, a playback queue can includeInternet radio and/or other streaming audio content items and be “inuse” when the playback zone or zone group is playing those items. Otherexamples are also possible.

When playback zones or zone groups are “grouped” or “ungrouped,”playback queues associated with the affected playback zones or zonegroups may be cleared or re-associated. For example, if a first playbackzone including a first playback queue is grouped with a second playbackzone including a second playback queue, the established zone group mayhave an associated playback queue that is initially empty, that containsaudio items from the first playback queue (such as if the secondplayback zone was added to the first playback zone), that contains audioitems from the second playback queue (such as if the first playback zonewas added to the second playback zone), or a combination of audio itemsfrom both the first and second playback queues. Subsequently, if theestablished zone group is ungrouped, the resulting first playback zonemay be re-associated with the previous first playback queue, or beassociated with a new playback queue that is empty or contains audioitems from the playback queue associated with the established zone groupbefore the established zone group was ungrouped. Similarly, theresulting second playback zone may be re-associated with the previoussecond playback queue, or be associated with a new playback queue thatis empty, or contains audio items from the playback queue associatedwith the established zone group before the established zone group wasungrouped. Other examples are also possible.

With reference still to FIGS. 4A and 4B, the graphical representationsof audio content in the playback queue region 446 (FIG. 4B) may includetrack titles, artist names, track lengths, and other relevantinformation associated with the audio content in the playback queue. Inone example, graphical representations of audio content may beselectable to bring up additional selectable icons to manage and/ormanipulate the playback queue and/or audio content represented in theplayback queue. For instance, a represented audio content may be removedfrom the playback queue, moved to a different position within theplayback queue, or selected to be played immediately, or after anycurrently playing audio content, among other possibilities. A playbackqueue associated with a playback zone or zone group may be stored in amemory on one or more playback devices in the playback zone or zonegroup, on a playback device that is not in the playback zone or zonegroup, and/or some other designated device. Playback of such a playbackqueue may involve one or more playback devices playing back media itemsof the queue, perhaps in sequential or random order.

The sources region 448 may include graphical representations ofselectable audio content sources and selectable voice assistantsassociated with a corresponding VAS. The VASes may be selectivelyassigned. In some examples, multiple VASes, such as AMAZON's ALEXA® andanother voice service, may be invokable by the same network microphonedevice. In some embodiments, a user may assign a VAS exclusively to oneor more network microphone devices, as discussed above. For example, auser may assign first VAS to one or both of the NMDs 102 a and 102 b inthe living room space shown in FIG. 1, and a second VAS to the NMD 103 fin the kitchen space. Other examples are possible.

d. Example Audio Content Sources

The audio sources in the sources region 448 may be audio content sourcesfrom which audio content may be retrieved and played by the selectedplayback zone or zone group. One or more playback devices in a zone orzone group may be configured to retrieve for playback audio content(e.g., according to a corresponding URI or URL for the audio content)from a variety of available audio content sources. In one example, audiocontent may be retrieved by a playback device directly from acorresponding audio content source (e.g., a line-in connection). Inanother example, audio content may be provided to a playback device overa network via one or more other playback devices or network devices.

Example audio content sources may include a memory of one or moreplayback devices in a media playback system such as the media playbacksystem 100 of FIG. 1, local music libraries on one or more networkdevices (such as a controller device, a network-enabled personalcomputer, or a networked-attached storage (NAS), for example), streamingaudio services providing audio content via the Internet (e.g., thecloud), or audio sources connected to the media playback system via aline-in input connection on a playback device or network devise, amongother possibilities.

In some embodiments, audio content sources may be regularly added orremoved from a media playback system such as the media playback system100 of FIG. 1. In one example, an indexing of audio items may beperformed whenever one or more audio content sources are added, removedor updated. Indexing of audio items may involve scanning foridentifiable audio items in all folders/directory shared over a networkaccessible by playback devices in the media playback system, andgenerating or updating an audio content database containing metadata(e.g., title, artist, album, track length, among others) and otherassociated information, such as a URI or URL for each identifiable audioitem found. Other examples for managing and maintaining audio contentsources may also be possible.

e. Example Network Microphone Devices

FIG. 5A is a functional block diagram showing additional features of oneor more of the NMDs 103 in accordance with aspects of the disclosure.The network microphone device shown in FIG. 5A may include componentsthat are generally similar to certain components of network microphonedevices described above, such as the processor 212 (FIG. 2), networkinterface 230 (FIG. 2), microphone(s) 224, and the memory 216. Althoughnot shown for purposes of clarity, a network microphone device mayinclude other components, such as speakers, amplifiers, signalprocessors, as discussed above.

The microphone(s) 224 may be a plurality of microphones arranged todetect sound in the environment of the network microphone device. In oneexample, the microphone(s) 224 may be arranged to detect audio from oneor more directions relative to the network microphone device. Themicrophone(s) 224 may be sensitive to a portion of a frequency range. Inone example, a first subset of the microphone(s) 224 may be sensitive toa first frequency range, while a second subset of the microphone(2) 224may be sensitive to a second frequency range. The microphone(s) 224 mayfurther be arranged to capture location information of an audio source(e.g., voice, audible sound) and/or to assist in filtering backgroundnoise. Notably, in some embodiments the microphone(s) 224 may have asingle microphone rather than a plurality of microphones.

A network microphone device may further include wake-word detector 552,beam former 553, acoustic echo canceller (AEC) 554, and speech/textconversion 555 (e.g., voice-to-text and text-to-voice). In variousembodiments, one or more of the wake-word detector 552, beam former 553,AEC 554, and speech/text conversion 555 may be a subcomponent of theprocessor 212, or implemented in software stored in memory 216 which isexecutable by the processor 212.

The wake-word detector 552 is configured to monitor and analyze receivedaudio to determine if any wake words are present in the audio. Thewake-word detector 552 may analyze the received audio using a wake worddetection algorithm. If the wake-word detector 552 detects a wake word,a network microphone device may process voice input contained in thereceived audio. Example wake word detection algorithms accept audio asinput and provide an indication of whether a wake word is present in theaudio. Many first- and third-party wake word detection algorithms areknown and commercially available. For instance, operators of a voiceservice may make their algorithm available for use in third-partydevices. Alternatively, an algorithm may be trained to detect certainwake-words.

In some embodiments, the wake-word detector 552 runs multiple wake worddetections algorithms on the received audio simultaneously (orsubstantially simultaneously). As noted above, different voice services(e.g. AMAZON's ALEXA®, APPLE's SIRI®, or MICROSOFT's CORTANA®) each usea different wake word for invoking their respective voice service. Tosupport multiple services, the wake word detector 552 may run thereceived audio through the wake word detection algorithm for eachsupported voice service in parallel.

The beam former 553 and AEC 554 are configured to detect an audio signaland determine aspects of voice input within the detect audio, such asthe direction, amplitude, frequency spectrum, etc. For example, the beamformer 553 and AEC 554 may be used in a process to determine anapproximate distance between a network microphone device and a userspeaking to the network microphone device. In another example, a networkmicrophone device may detective a relative proximity of a user toanother network microphone device in a media playback system.

FIG. 5B is a diagram of an example voice input in accordance withaspects of the disclosure. The voice input may be captured by a networkmicrophone device, such as by one or more of the NMDs 103 shown inFIG. 1. The voice input may include a wake word portion 557 a and avoice utterance portion 557 b (collectively “voice input 557”). In someembodiments, the wake word 557 a can be a known wake word, such as“Alexa,” which is associated with AMAZON's ALEXA®).

In some embodiments, a network microphone device may output an audibleand/or visible response upon detection of the wake word portion 557 a.In addition or alternately, a network microphone device may output anaudible and/or visible response after processing a voice input and/or aseries of voice inputs (e.g., in the case of a multi-turn request).

The voice utterance portion 557 b may include, for example, one or morespoken commands 558 (identified individually as a first command 558 aand a second command 558 b) and one or more spoken keywords 559(identified individually as a first keyword 559 a and a second keyword559 b). In one example, the first command 557 a can be a command to playmusic, such as a specific song, album, playlist, etc. In this example,the keywords 559 may be one or words identifying one or more zones inwhich the music is to be played, such as the Living Room and the DiningRoom shown in FIG. 1. In some examples, the voice utterance portion 557b can include other information, such as detected pauses (e.g., periodsof non-speech) between words spoken by a user, as shown in FIG. 5B. Thepauses may demarcate the locations of separate commands, keywords, orother information spoke by the user within the voice utterance portion557 b.

In some embodiments, the media playback system 100 is configured totemporarily reduce the volume of audio content that it is playing whiledetecting the wake word portion 557 a. The media playback system 100 mayrestore the volume after processing the voice input 557, as shown inFIG. 5B. Such a process can be referred to as ducking, examples of whichare disclosed in U.S. patent application Ser. No. 15/277,810 filed Sep.27, 2016 and titled “Audio Playback Settings for Voice Interaction,”which is incorporated herein by reference in its entirety.

f. Example Network System

FIG. 6 is a functional block diagram showing additional details of theremote computing device(s) 105 in FIG. 1. In various embodiments, theremote computing device(s) 105 may receive voice inputs from one or moreof the NMDs 103 over the WAN 107 shown in FIG. 1. For purposes ofillustration, selected communication paths of the voice input 557 (FIG.5B) are represented by arrows in FIG. 6. In one embodiment, the voiceinput 557 processed by the remote computing device(s) 105 may includethe voice utterance portion 557 b (FIG. 5B). In another embodiment, theprocessed voice input 557 may include both the voice utterance portion557 b and the wake word 557 a (FIG. 5B)

The remote computing device(s) 105 include a system controller 612comprising one or more processors, an intent engine 602, and a memory616. The memory 616 may be a tangible computer-readable mediumconfigured to store instructions executable by the system controller 612and/or one or more of the playback, network microphone, and/orcontroller devices 102-104.

The intent engine 662 is configured to process a voice input anddetermine an intent of the input. In some embodiments, the intent engine662 may be a subcomponent of the system controller 612. The intentengine 662 may interact with one or more database(s), such as one ormore VAS database(s) 664, to process voice inputs. The VAS database(s)664 may reside in the memory 616 or elsewhere, such as in memory of oneor more of the playback, network microphone, and/or controller devices102-104. In some embodiments, the VAS database(s) 664 may be updated foradaptive learning and feedback based on the voice input processing. TheVAS database(s) 664 may store various user data, analytics, catalogs,and other information for NLU-related and/or other processing.

The remote computing device(s) 105 may exchange various feedback,information, instructions, and/or related data with the variousplayback, network microphone, and/or controller devices 102-104 of themedia playback system 100. Such exchanges may be related to orindependent of transmitted messages containing voice inputs. In someembodiments, the remote computing device(s) 105 and the media playbacksystem 100 may exchange data via communication paths as described hereinand/or using a metadata exchange channel as described in U.S.application Ser. No. 15/131,244 filed Apr. 18, 2016, and titled“Metadata exchange involving a networked playback system and a networkedmicrophone system, which is incorporated by reference in its entirety.

Processing of a voice input by devices of the media playback system 100may be carried out at least partially in parallel with processing of thevoice input by the remote computing device(s) 105. Additionally, thespeech/text conversion components 555 of a network microphone device mayconvert responses from the remote computing device(s) 105 to speech foraudible output via one or more speakers.

III. Example Acoustic Echo Cancellation Techniques

As discussed above, some embodiments described herein involve acousticecho cancellation. FIG. 8A is a functional block diagram of an acousticecho cancellation pipeline 800 a configured to be implemented within aplayback device that includes a NMD, such as NMDs 103 a-e. By way ofexample, the acoustic echo cancellation pipeline 800 a is described asbeing implemented within the playback device 102 of FIG. 2. However, inother implementations, acoustic echo cancellation pipeline 800 a may beimplemented in an NMD that is not necessarily a playback device (e.g., adevice that doesn't include speakers, or includes relatively low-outputspeakers configured to provide audio feedback to voice inputs), such asNMDs 103 f-g.

In operation, acoustic echo cancellation pipeline 800 a may be activatedwhen the playback device 102 is playing back audio content. As notedabove, acoustic echo cancellation can be used to remove acoustic echo(i.e., the sound of the audio playback and reflections and/or otheracoustic artifacts from the acoustic environment) from the signalcaptured by microphone(s) of the networked microphone device. Wheneffective, acoustic echo cancellation improves the signal-to-noise ratioof a voice input with respect to other sound within the acousticenvironment. In some implementations, when audio playback is paused orotherwise idle, the acoustic echo cancellation pipeline 800 a isbypassed or otherwise disabled.

As shown in FIG. 8A, the microphone array 224 (FIG. 2) is configured tocapture a “measured signal,” which is an input to the acoustic echocancellation pipeline 800 a. As described above in reference to FIGS. 2and 5, the microphone array 224 can be configured to capture audiowithin an acoustic environment in an attempt to detect voice inputs(e.g., wake-words and/or utterances) from one or more users. When theplayback device 102 plays back audio content via speakers 222 (FIG. 2),the microphone array 224 can capture audio that also includes audiosignals representing sound produced by speakers 222 in playing back theaudio content, as well as other sound being produced within the acousticenvironment.

At block 870 a, the measured signal is pre-processed in advance ofacoustic echo cancellation. Pre-processing of the measured signal mayinvolve analog-to-digital conversion of the microphone array signals.Other pre-processing may include sample rate conversion, de-jittering,de-interleaving, or filtering, among other examples. The term “measuredsignal” is generally used to refer to the signal captured by themicrophone array 224 before and after any pre-processing.

As shown in FIG. 8A, another input to the acoustic echo cancellationpipeline 800 a is a “reference signal.” The reference signal canrepresent the audio content being played back by the speakers 222 (FIG.2). As shown, the reference signal is routed from the audio processingcomponents 218. In an effort to more closely represent the audio contentbeing played back by the speakers 222, the reference signal may be takenfrom a point in an audio processing pipeline of the audio processingcomponents 218 that closely represents the expected analog audio outputof speakers 222. Since each stage of an audio processing pipeline mayintroduce artifacts, the point in the audio processing pipeline of theaudio processing components 218 that closely represents the expectedanalog audio output of the speakers 222 is typically near the end of thepipeline.

As noted above, although the acoustic echo cancellation pipeline 800 ais shown by way of example as being illustrated within the playbackdevice 102, the acoustic echo cancellation pipeline 800 a mayalternatively be implemented within a dedicated NMD such as NMD 103 f-gof FIG. 1. In such examples, the reference signal may sent from theplayback device(s) that are playing back audio content to the NMD,perhaps via a network interface or other communications interface, suchas a line-in interface.

At block 870 b, the reference signal is pre-processed in advance ofacoustic echo cancellation. Pre-processing of the reference signal mayinvolve sample rate conversion, de-jittering, de-interleaving,time-delay, or filtering, among other examples. The term “measuredsignal” is generally used to refer to the signal captured by themicrophone array 224 before and after any pre-processing.

Pre-processing the measured signal and the reference signals readies thesignals for mixing during acoustic echo cancellation. For instance,since audio content is output by the speakers 222 before the microphonearray 224 captures a representation of that same content, time-delay maybe introduced to the reference signal to time-align the measured andreference signals. Similarly, since the respective sample rates ofanalog-to-digital conversation of the analog microphone signals and thereference signal from the audio processing components 218 may bedifferent, sample rate conversation of one or both of the signals mayconvert the signal(s) into the same or otherwise compatible samplerates. Other similar pre-processing may be performed in blocks 870 a and870 b to render the measured signals and reference signals compatible.

At block 871 a, the measured and reference signals are converted intothe short-time Fourier transform domain. Acoustic echo cancellation inthe STFT domain may lessen the processing requirements of acoustic echocancellation as compared with acoustic echo cancellation in otherdomains, such as the Frequency-Dependent Adaptive Filter (“FDAF”)domain. As such, by processing in the STFT domain, additional techniquesfor acoustic echo cancellation may become practical.

As those of ordinary skill in the art will appreciate, a STFT is atransform used to determine the sinusoidal frequency and phase contentof local sections (referred to as “frames” or “blocks”) of a signal asit changes over time. To compute a STFTs of the measured and referencesignals, each signal is divided into a plurality of frames. In anexample implementation, each frame is 16 milliseconds (ms) long. Thenumber of samples in a 16 ms frame may vary based on the sample rate ofthe measured and reference signals.

Given a signal x(n), the signal is transformed to the STFT domain by:X _(k)[m]=Σ_(n=0) ^(N−1) x[n+mR]w _(A)[n]ω_(N) ^(kn),where k is the frequency index, m is the frame index, N is the framesize, R is the frame shift size, w_(A)[n] is an analysis window of sizeN, and

$~{\omega_{N} = {\exp\mspace{11mu}{\left( {{- j}\frac{2\pi}{N}} \right).}}}$

Referring now to AEC 554 (FIG. 5A), after being converted into the STFTdomain, the measured and reference signals are provided as input to theAEC 554, as shown in FIG. 8A. The acoustic echo cancellation performedby the AEC 554 on the measured signal is an iterative process. Eachiteration of the AEC 554 processes a respective frame of the measuredsignal using a respective frame of the reference signal. Such processingincludes passing a frame of the reference signal through the adaptivefilter 872 to yield a frame of a model signal. The adaptive filter 872is intended to transform the reference signal into the measured signalwith minimal error. In other words, the model signal is an estimate ofthe acoustic echo.

To cancel the acoustic echo from the measured signal, the measuredsignal and the model signal are provided to a redaction function 873.Redaction function 873 redacts the model signal from the measuredsignal, thereby cancelling the estimated acoustic echo from the measuredsignal yielding an output signal. In some examples, the redactionfunction 873 redacts the model signal from the measured signal byinverting the model signal via inverter 874 and mixing the invertedmodel signal with a frame of the measured signal with mixer 875. Ineffect, this mixing removes the audio playback (the reference signal)from the measured signal, thereby cancelling the echo (i.e., the audioplayback and associated acoustic effects) from the measured signal.Alternate implementations may use other techniques for redaction.

At block 871 b, the output signal of AEC 554 is transformed back byapplying the inverse STFT. The inverse STFT is applied by:x[n]=Σ_(m)Σ_(k=0) ^(N−1) X _(k)[m]w _(S)[n−mR]ω_(N) ^(−k(n−mR)),where w_(s)[n] is a synthesis window.

After block 871 b, the output signal is provided to a voice inputprocessing pipeline at block 880. Voice input processing may involvewake-word detection, voice/speech conversion, and/or sending one or morevoice utterances to a voice assistant service, among other examples.

Turning now in more detail to internal aspects of the AEC 554, at block872, the reference signal in the STFT domain is passed through theadaptive filter 872. As noted above, the adaptive filter 872 is atransfer function that adapts during each iteration of the AEC 554 in anattempt to transform the reference signal into the measured signal withdiminishing error. Passing a frame of the reference signal throughadaptive filter 872 yields a frame of a model signal. The model signalis an estimate of the acoustic echo of the reference signal (i.e., theaudio that is being cancelled).

Within examples, adaptive filter 872 implements multi-delay adaptivefiltering. To illustrate example multi-delay adaptive filtering, let Nbe the multi-delay filter (MDF) block size, K be the number of blocksand F_(2N) denote the 2N×2N Fourier transform matrix, and thefrequency-domain signals for frame m are:e(m)=F _(2N)[0_(1×N) ,e(mN), . . . ,e(mN+N−1)]^(T),X _(k)(m)=diag{F _(2N)[x((m−k−1)N−1), . . . ,x((m−k+1)N−1)]^(T)}d(m)=F _(2N)[0_(1×N) ,d(mN), . . . ,d(mN+N−1)]^(T),where d(m) is the modeled signal, e(m) is the modeling error, andX_(k)(m) is the measured signal. The MDF algorithm then becomes:e(m)=d(m)−ŷ(m),ŷ(m)=Σ_(k=0) ^(K−1) G ₁ X _(k)(m)ĥ _(k)(m−1),with model update:∀k:ĥ _(k)(m)=(m−1)+G ₂μ_(m)(m)∇ĥ _(k)(m), and∇ĥ _(k)(m)=P _(X) _(k) _(X) _(k) ⁻¹(m)X _(k) ^(H)(m)e(m),where G₁ and G₂ are matrices which select certain time-domain parts ofthe signal in the frequency domain,

${G_{1} = {{F_{2N}\begin{bmatrix}O_{N \times N} & O_{N \times N} \\O_{N \times N} & I_{N \times N}\end{bmatrix}}F_{2N}^{- 1}}},{and}$ $G_{2} = {{F_{2N}\begin{bmatrix}I_{N \times N} & O_{N \times N} \\O_{N \times N} & O_{N \times N}\end{bmatrix}}{F_{2N}^{- 1}.}}$The matrix P_(X) _(k) _(X) _(k) (m)=X_(k) ^(H)(m)X_(k)(m) is a diagonalapproximation of the input power spectral density matrix. To reduce thevariance of the power spectrum estimate, the instantaneous powerestimate may be substituted by its smoothed version,P _(X) _(k) _(X) _(k) (m)=βP _(X) _(k) _(X) _(k) (m−1)+(1−β)X _(k)^(H)(m)X _(k)(m),where β is the smoothing term. This example also assumes a fixedstep-size (how much the filter is adapted during each iteration) foreach partition μ(m)=μ₀I, however the step size may be varied in someimplementations.

Example implementations of adaptive filter 872 implement cross-bandfiltering. To illustrate such filtering, let y[n] be the near-endmeasured signal, which includes the near-end speech and/or noise v[n]mixed with the acoustic echo d[n]=h[n]*x[n], where h[n] is the impulseresponse of the system, x[n] is the far-end reference signal, and * isthe convolution operator. Let x[m]=[x[mR], . . . x[mR+N−1]]^(T) be them^(th) reference signal vector, w_(A)=[w_(A)[0], . . . , w_(A)[N−1]]^(T)be the analysis window vector, (F)_(k+1,n+1)=w_(N) ^(kn), k, n=0 . . . ,N−1 be the N×N discrete Fourier transform matrix, andx[m]=F(w_(A)∘x[m])=[X₀[m], . . . , X_(N−1)[m]]^(T) be the DFT of thewindowed reference signal vector, where ∘ is the Hadamard (element-wise)product operator and {⋅}^(T) is the transpose operator.

As noted above, passing a frame of the reference signal through theadaptive filter 872 yields a frame of a model signal. Given a transferfunction H, the acoustic echo can be represented in the STFT domain asd [m]=Σ_(i=0) ^(M−1) H _(i)[m−1] x [m−i],where d[m] is the DFT of the m^(th) frame echo signal, H_(i) is thei^(th) impulse response matrix (i.e., the filter for the m^(th)iteration of the AEC 554), and M is the filter length in the STFTdomain.

Given the foregoing, acoustic echo cancellation by the AEC 554 can beexpressed in the STFT domain as:x [m]=F(w _(A)∘[x[mR], . . . ,x[mR+N−1]]^(T)), where x [m] is thereference signal,y [m]=F(w _(A)∘[y[mR], . . . ,y[mR+N−1]]^(T)), where y [m] is themeasured signal,and

e[m]=y[m]−{circumflex over (d)}[m]=y[m]−Σ_(i=0) ^(M−1)Ĥ_(i)[m−1]x[m−i],where e[m] is the output signal. As noted above, the redaction function808 redacts the model signal {circumflex over (d)}[m] from the measuredsignal.

When noise and/or speech are present in the measured signal, the errorsignal vector is given bye [m]= v [m]+ d [m]− {circumflex over (d)} [m]= v [m]+ b [m],where v[m] and b[m] is the noise vector and the noise-free error signalvector (a.k.a., the true error signal), respectively, in the STFTdomain. Since the error signal e[m] deviates from the true, noise-free,echo signal vector b[m], the adaptive filter may diverge from theoptimal solution due to near-end interference (e.g., one or more secondvoices in a double-talk condition). Some implementations may halt orotherwise disable adaptation of the filter during such conditions toavoid introducing noise into the signal, possibly using a double-talkdetector. However, such implementations have the disadvantage thatacoustic echo is not effectively cancelled from the measured signalwhile the AEC filter is disabled (or not adapting). To toleratesignificant near-end interference v[m] (e.g., double-talk), one or morerobustness constraints are introduced to stabilize the filter update.

Namely, at block 876, the AEC 554 estimates the true error signal. Thetrue error signal b[m] is the difference between the actual acousticecho d[m] and the estimated acoustic echo {circumflex over (d)}[m]produced by the adaptive filter 872. The output signal, renamed as theerror signal, which includes the audio in the room other than theacoustic echo (e.g., one or more voices) as well as the true errorsignal, is provided as input to block 876. Ultimately, the true errorsignal is used in determining an update filter at block 878, which issummed with the adaptive filter 872 to yield the adaptive filter for thenext iteration.

In some examples, estimating the true error signal may involve limitingthe error if it exceeds a certain magnitude threshold. Such limiting mayprevent unwanted divergence in noise conditions (e.g., double talk).Limiting the error may involve error recovery non-linearity (ERN) whichcan express the estimated true error signal ϕ(E_(k)(m)) as a non-linearclipping function:

${\phi\left( {E_{k}(m)} \right)} = \left\{ {\begin{matrix}{{\frac{T_{k}}{\left| E_{k} \right|}{E_{k}\lbrack m\rbrack}},} & \left| {E_{k}\lbrack m\rbrack} \middle| {\geq {T_{k}\lbrack m\rbrack}} \right. \\{{E_{k}\lbrack m\rbrack},} & {otherwise}\end{matrix}.} \right.$This non-linear clipping function limits the error signal when itsmagnitude is above a certain threshold T_(k)[m]. This threshold isestimated based on the near-end (measured) signal statistics and isapproximated by T_(k)[m]=√{square root over (S_(ee,k)[m])} withS _(ee,k)[m]≡E{|E _(k)[m]|² }≈βS _(ee,k)[m−1]+(1−β)|E _(k)[m]|²,where S_(ee,k)[m] is the power spectral density (PSD) of the errorsignal, E{⋅} is the expectation operator, and 0<<β<<1 is a forgettingfactor. This non-linear clipping function is provided by way of example.Other functions may be implemented as well to estimate the true errorsignal.

Given the foregoing, the true error signal ϕ(E_(k)(m)) can be determinedas follows:

${{{\underset{\_}{s}}_{xx}\lbrack m\rbrack} = {{\beta{{\underset{\_}{s}}_{\;_{xx}}\left\lbrack {m - 1} \right\rbrack}} + {\left( {1 - \beta} \right)\left( {{{\underset{\_}{x}\lbrack m\rbrack} \circ \underset{\_}{x}} \star \lbrack m\rbrack} \right)}}},{{{\underset{\_}{s}}_{ee}\lbrack m\rbrack} = {{\beta{{\underset{\_}{s}}_{\;_{ee}}\left\lbrack {m - 1} \right\rbrack}} + {\left( {1 - \beta} \right)\left( {{{\underset{\_}{e}\lbrack m\rbrack} \circ \underset{\_}{e}} \star \lbrack m\rbrack} \right)}}},{{\phi\left( {E_{k}(m)} \right)} = \left\{ {\begin{matrix}{{\frac{\sqrt{S_{{ee},k}\lbrack m\rbrack}}{\left| E_{k} \right|}{E_{k}\lbrack m\rbrack}},} & \left| {E_{k}\lbrack m\rbrack} \middle| {\geq \sqrt{S_{{ee},k}\lbrack m\rbrack}} \right. \\{{E_{k}\lbrack m\rbrack},} & {otherwise}\end{matrix}.} \right.}$Recall that x[m] represents the reference signal and e[m] represents theerror signal, which is the measured signal with the model signalredacted.

At block 877, the normalized least mean square of the true error signalis determined. In the normalized least square algorithm, the least meansquare of the error is normalized with the power of the input (e.g., thereference signal). This has the effect of varying the step size of thealgorithm to make it more noise-robust.

Normalization with respect to the power of the input can be expressed asn _(xx)[m]=( s _(xx)[m]+δ1_(N×1))º⁽⁻¹⁾,where {⋅}º⁽⁻¹⁾ is the Hadamard (element-wise) inverse operator,1_(N×1)=[1, . . . , 1]^(T), δ is a regularization term ands s_(xx)[m]=E{x[m]∘x*[m]}≡[S_(xx,0)[m], . . . , S_(xx,N−1)[m]]^(T) is thePSD vector of the reference signal with {⋅}* being the element-wisecomplex conjugate operator.

In some cases, noise robustness may be further improved by applying afrequency dependent regularization term. For instance, such a term maybe expressed as:

${\delta_{k}\lbrack m\rbrack} = {\gamma{\frac{s_{{ee},k}^{2}\lbrack m\rbrack}{s_{{xx},k}\lbrack m\rbrack}.}}$This term scales down the step-size automatically when the near-end(measured) signal is large, helping to keep adaption of the filterrobust.

At block 878, an update filter is determined. As noted above,ultimately, the update filter is summed with the filter used in thecurrent iteration of the AEC 554 to yield the filter for the nextiteration of the AEC 554. Generally, during the first iterations of theAEC 554, some error exists in the cancellation of the echo from themeasured signal. However, over successive iterations of the AEC 554,this error is diminished. In particular, during each iteration of theAEC 554, the adaptive filter 872 is updated for the next iteration basedon error from the current iteration. In this way, during successiveiterations of the AEC 554, the AEC 554 mathematically converges to acancellation of the audio playback by the speakers 222 (FIG. 2). In thisway, the filter adapts during a successive iteration of the AEC based onerror from the previous iteration.

In the first iteration of the AEC 554, an initial filter is utilized, asno adaptation has yet occurred. In some implementations, the initialfilter is a transfer function representing the acoustic coupling betweenspeakers 222 and microphones 224. In some embodiments, the initialfilter comprises a transfer function generated using measurementsperformed in an anechoic chamber. The generated transfer function canrepresent an acoustic coupling between the speakers 222 and themicrophones 224 without any room effect. Such an initial filter could beused in any acoustic environment. Alternatively, in an effort to startthe adaptive filter in a state that more closely matches the actualacoustic environment in which the playback device is located, a transferfunction representing an acoustic coupling between the speakers 222 andthe microphones 224 may be determined during a calibration procedurethat involves microphones 224 recording audio output by speakers 222 ina quiet room (e.g., with minimal noise). Other initial filters may beused as well, although a filter that poorly represents the acousticcoupling between the speakers 222 and the microphones 224 may provide aless optimal starting point for the AEC 554 and result in convergencerequiring additional iterations of the AEC 554.

In subsequent iterations of the AEC, the adaptive filter 872 cancontinue to adapt. During each n^(th) iteration of the AEC, an n+1^(th)instance of the adaptive filter 806 is determined for the next iterationof the AEC. In particular, during the n^(th) iteration of the AEC 554,the n^(th) instance of the adaptive filter 872 is summed with an n^(th)update filter to yield the n+1^(th) instance of the adaptive filter 872.The n^(th) update filter is based on the modelling error of the filterduring the n^(th) iteration.

To illustrate, let Ĥ be an adaptive filter matrix. For a filter having Kblocks, to improve the modeling accuracy, 2K cross-terms, or 2Koff-diagonal bands are added around the main diagonal terms of H withoutincreasing the computational complexity to an impractical extent. Recallthat K In this example, Ĥ has 2K+1 diagonal bands. The model signal(i.e., the estimated acoustic echo) can be written as{circumflex over (d)} [m]=Σ_(i=0) ^(M−1) Ĥ _(i)[m−1] x [m−i],and the adaptive filter matrix can be updated from iteration toiteration usingĤ _(i)[m]=Ĥ _(i)[m−1]+G∘ΔĤ _(i)[m],i=0, . . . M−1,where ΔĤ_(i)[m] is an update matrix for the filter coefficients matrixand G=Σ_(k=−K) ^(K)P^(k) is a matrix that selects the 2K+1 diagonalbands. P is a permutation matrix defined as

$P \equiv \begin{bmatrix}0 & \ldots & \ldots & 0 & 1 \\1 & \ddots & \vdots & 0 & 0 \\0 & \ddots & \ddots & \vdots & \vdots \\\vdots & \ddots & \ddots & 0 & 0 \\0 & \ldots & 0 & 1 & 0\end{bmatrix}$The matrix G limits the number of crossband filters that are useful forsystem identification in the STFT domain since increasing the number ofcrossband filters does not necessarily lead to a lower steady-stateerror.

As noted above, the n^(th) update filter is based on the modelling errorof the filter during the n^(th) iteration. Using a least mean squaresalgorithm, the update filter is given byĤ _(i) ^(LMS)[m]=μ e [m] x ^(H)[m−i],where e[m]=y[m]−{circumflex over (d)}[m] is the error signal vector inthe STFT domain, μ>0 is a step-size, and {⋅}^(H) is the Hermitiantranspose operator. As compared with FDAD-type algorithms, this updatefilter takes into account the contribution of the cross-frequencycomponents of the reference signal without relying on the DFT and IDFTfor cancelling the aliased components, which allows for a simplifiedprocessing pipeline with less complexity.

As noted above, as an alternative to the least mean squares, anormalized least mean squares algorithm may be implemented to improvenoise-robustness. Using the NMLS from block 818, the update filter isgiven by:ΔĤ _(i) ^(NLMS)[m]=μ e [m]( n [m]∘ x [m−1])^(H),where the reference signal is normalized by its signal power beforebeing multiplied by the error signal in block 818. Note that eachelement of the NLMS update matrix is given as:

$\left( {\Delta\;{{\hat{H}}_{i}^{NLMS}\lbrack m\rbrack}} \right)_{{k + 1},{l + 1}} = {\mu\frac{{\phi\left( {E_{k}\lbrack m\rbrack} \right)}{X_{l}^{*}\left\lbrack {m - 1} \right\rbrack}}{{S_{{xx},l}\lbrack m\rbrack} + \delta}}$In implementations in which the cross-frequency dependent regularizationterm is utilized, then then the NMLS update matrix is given by:

$\left( {\Delta\;{{\hat{H}}_{i}^{NLMS}\lbrack m\rbrack}} \right)_{{k + 1},{l + 1}} = {\mu{\frac{{\phi\left( {E_{k}\lbrack m\rbrack} \right)}{X_{l}^{*}\left\lbrack {m - 1} \right\rbrack}}{{S_{{xx},l}\lbrack m\rbrack} + {\delta_{k,l}\lbrack m\rbrack}}.}}$

Given the foregoing, a noise-robust adaptive step size for the AEC canbe expressed in matrix form as:

$\left( {M\lbrack m\rbrack} \right)_{{k + 1},{l + 1}} = {\frac{S_{{xx},l}\lbrack m\rbrack}{{S_{{xx},l}^{2}\lbrack m\rbrack} + {\gamma\;{S_{{ee},k}^{2}\lbrack m\rbrack}}}.}$

Then the update matrix is given as:ΔĤ _(i)[m]=μ M [m]∘(ϕ( e [m]) x ^(H)[m−1]),i=0, . . . ,M−1where ϕ(e[m])≡[ϕ(E₀ [m]), . . . , ϕ(E_(N−1)[m−1])]^(T) is the estimateof the true error signal vector after applying ERN.

As noted above, particular, during the n^(th) iteration of the AEC 554,the n^(th) instance of the adaptive filter 872 is summed with an n^(th)update filter to yield the n+1^(th) instance of the adaptive filter 872.Given the example above, the adaptive filter is represented as:Ĥ _(i)[m]=Ĥ _(i)[m−1]+G∘ΔĤ _(i)[m],i=0, . . . ,M−1

At block 879, a sparsity criterion is applied to the output of theupdate filter 878. A sparsity criterion may deactivate inactive portionsof the filter. This allows use of a high order multi delay filter whereonly the partitions that correspond to the actual model are active,thereby reducing computation requirements. Although FIG. 8A suggeststhat the sparsity criterion is applied after determination of the updatefilter 879, the sparsity criterion may be applied either before or afterthe update filter.

The sparsity criterion may implemented as a thresholding operator:

${T_{ɛ}\left( h_{j} \right)} = \left\{ {\begin{matrix}{0,} & {{h_{j}}_{1} \leq ɛ_{j}} \\{h_{j},} & {{h_{j}}_{1} > ɛ_{j}}\end{matrix},} \right.$which distinguishes between active and inactive partitions. Withinexamples, ε_(j) is in the order of the estimated noise level normalizedfor the block length. T_(ε)(h_(j)) attempts to solve

 _(h_(j)(m) ∈ ℝ)_(B_(j)N)^(  min )e_(j)(m)₂² + γ_(j)h_(j)(m)₁,where γ_(i) controls the sparsity of the j^(th) filter. In someexamples, the thresholding operator can be applied to the filter updatestep of the NMLS algorithm, which then becomes:∀j:ĥ _(j)(m)=T _(ε)(ĥ _(j)(m−1)+G ₂ _(j) μ₀(m)∇ĥ _(j)(m)).Applying the sparsity constraint during each iteration of the AECresults in a Landweber iteration with thresholding, which contributes tothe noise robustness of the AEC.

In example implementations, acoustic echo cancellation pipeline 800 amay be integrated into an audio processing pipeline that includesadditional audio processing of microphone-captured audio such as beamforming, blind source separation, and frequency gating before themicrophone-captured audio is processed as a voice input to a voiceservice.

FIG. 8B is a functional block diagram of an audio processing pipeline800 b that integrates acoustic echo cancellation pipeline 800 a. Asshown in FIG. 8B, other voice processing functions such as beamforming(via the beam former 553), blind signal separation (via the blind signalseparator 882), and frequency gating (via the frequency gating component881) are performed in the STFT domain using the AEC signals. Byperforming these functions in conjunction with AEC in the STFT domain,the overall audio processing pipeline can be less complex thanconventional AEC approaches as fewer applications of the DFT and inverseDFT are involved, reducing the overall computational complexity of theaudio processing pipeline.

IV. Example Acoustic Echo Cancellation

As discussed above, embodiments described herein may involve acousticecho cancellation. FIG. 9 is a flow diagram of an example implementation900 by which a system (e.g., the playback device 102, the NMD 103,and/or the control device 104) may perform noise-robust acoustic echocancellation in the STFT domain. In some embodiments, the implementation900 can comprise instructions stored on a memory (e.g., the memory 216and/or the memory 316) and executable by one or more processors (e.g.,the processor 212 and/or the processor 312).

a. Causing One or More Speakers to Play Back Audio Content

At block 902, the implementation 900 causes one or more speakers to playback audio content. For instance, the implementation 900 can beconfigured to cause a playback device (e.g., the playback device 102 ofFIG. 2) to play back audio content via one or more speakers (e.g., thespeakers 222). Example audio content includes audio tracks, audio withvideo (e.g., home theatre), streaming audio content, and many others.Prior to playback, the playback device may process and/or amplify theaudio content via an audio stage, which may include audio processingcomponents (e.g., the audio processing components 218 of FIG. 2) and/orone or more audio amplifiers (e.g., the audio amplifiers 220 of FIG. 2).

As noted above, the audio content may be designed for playback by theplayback device 102 by another device. For instance, a controller device(e.g., the controller devices 103 a and/or 103 b of FIG. 1, the controldevice 104 of FIG. 3) may instruct a playback device to play backcertain audio content by causing that content to be placed in a playbackqueue of the playback device. Placing an audio track or other audiocontent into such a queue can cause the playback device to retrieve theaudio content after playback is initiated via a control on thecontroller device 104 and/or on the playback device 102 itself (e.g.,via a Play/Pause button).

b. Capture Audio Within Acoustic Environment

At block 904, the implementation 900 captures audio within the acousticenvironment. For instance, the implementation 900 can be configures tocapture audio within an acoustic environment via an NMD (e.g., the NMD103 of FIG. 2) having one or more microphones (e.g., two or moremicrophones of microphone array 224). Capturing audio may involverecording audio within an acoustic environment and/or processing of therecorded audio (e.g., analog-to-digital conversation).

In some embodiments, the implementation 900 is configured to captureaudio within an acoustic environment while one or more playback devicesare also playing back audio content within the acoustic environment. Thecaptured audio can include, for example, audio signals representingacoustic echoes caused by playback of the audio content in the acousticenvironment. The captured audio may also include audio signalsrepresenting speech (e.g., voice input to a voice assistant service orother speech such as conversation) as well as other sounds or noisepresent in the acoustic environment.

c. Receive Output Signal From Audio Stage

At block 906, the implementation 900 receives an output signal from theaudio stage. For instance, the implementation 900 can be configured toreceive an output signal from the audio stage of the playback device200. As described above in reference to FIG. 8A, the output signal canrepresent audio content played back by the playback device 200.Ultimately, the output signal becomes a reference signal for acousticecho cancellation. Accordingly, within examples, the output signal isrouted from a point in the audio pipeline of the playback device thatclosely represents the actual output produced by the speakers 224 of theplayback device 200. Since each stage of an audio processing pipelinemay introduce its own artifacts, the point in the audio processingpipeline that closely represents the expected analog audio output of thespeakers is typically near the end of the pipeline.

In some embodiments, the implementation 900 is configured to receive theoutput signal internally from the audio pipeline such as, for example,when an NMD in is consolidated in a playback device (e.g., as with NMD103 of playback device 102 shown in FIG. 2). In other embodiments,however, the implementation 900 is configured to receive the outputsignal via an input interface, such as a network interface (e.g.,network interface 230) or a line-in interface, among other examples.

d. Determine Measured and Referenced Signals in STFT Domain

At block 908, the implementation 900 is configured to determine measuredand reference signals in an STFT domain. For instance, the system maydetermine a measured signal based on the captured audio and a referencesignal based on the output signal from the audio stage of the playbackdevice.

Determining the measured signal may involve processing and/orconditioning of the captured audio prior to acoustic echo cancellation.As described above in reference to FIGS. 8A and 8B, acoustic echocancellation in the STFT domain may occur on a frame-by-frame basis,with each frame including a series of samples (e.g., 16 ms of samples).As such the measured signal may include a series of frames representingthe captured audio within the acoustic environment. Frames of thecaptured audio may be pre-processed (e.g., as described with respect toblock 870 a of FIG. 8A) and then converted into the STFT domain (e.g.,as described with respect to block 871 a of FIG. 8A) to yield a measuredsignal for input to an AEC (e.g., AEC 554).

Determining the reference signal may involve similarly involveprocessing and/or conditioning prior to AEC. Like the measured signal,the output signal from the audio stage may be divided into a series offrames representing portions of a reference signal. Frames of the outputsignal may be pre-processed (e.g., as described with respect to block870 b of FIG. 8A) and then converted into the STFT domain (e.g., asdescribed with respect to block 871 a of FIG. 8A) to yield a referencesignal for input to an AEC (e.g., AEC 554).

e. Determine Frames of Output Signal

At block 910, the implementation 900 is configured to determine framesof an output signal from an AEC. In some embodiments, for example, theimplementation 900 comprises an AEC (such as the AEC 554 of FIGS. 8A and8B) configured to determine frames of an output signal during eachiteration of the AEC. As described above with respect to FIG. 8A, duringeach n^(th) iteration of AEC 554, an n^(th) frame of the referencesignal through an n^(th) instance of adaptive filter 872 yielding ann^(th) frame of a model signal. Then the n^(th) frame of the outputsignal is generated by redacting the n^(th) frame of the model signalfrom the n^(th) frame of the measured signal (e.g., using redactionfunction 808).

As further described above, an output signal e[m] can be defined inexample implementations ase [m]= y [m]− {circumflex over (d)} [m]= y [m]−Σ_(i=0) ^(M−1) Ĥ_(i)[m−1] x [m−i],where the reference signal x[m]=F(w_(A)∘[x[mR], . . . , x[mR+N−1]]^(T))and the measured signal y[m]=F(w_(A)∘[y[mR], . . . , y[mR+N−1]]^(T)).f. Update Adaptive Filter During Each Iteration of AEC

At block 912, the implementation 900 is configured to update theadaptive filter during one or more iterations of the AEC as described,for example, with reference to AEC 554 in FIG. 8A. Recall that, duringeach n^(th) iteration, an n^(th) update matrix is determined based on a“true” error signal representing a difference between the n^(th) frameof the model signal and the n^(th) frame of the reference signal lessaudio signals representing sound from sources other than an n^(th) frameof the audio signals representing sound produced by the one or morespeakers in playing back the n^(th) frame of the reference signal. Thiserror signal can be referred to as the true error signal and can bedetermined using an ERN function that limits the error signal to athreshold magnitude, as described with respect to block 876 in FIG. 8A.The n+1^(th) instance of the adaptive filter for the next iteration ofthe AEC 554 is generated by summing the n^(th) instance of the adaptivefilter with the n^(th) update filter.

In an effort to increase robustness of the AEC 554 in view ofsignificant noise, the adaptive filter may adapt according to a NMLSalgorithm. Under such an algorithm, the true error signal may benormalized according to the power of the input (e.g., the referencesignal), as described in block 877 of FIG. 8A. Further, AEC 554 mayapply a sparse partition criterion that deactivates inactive portions ofthe adaptive filter (e.g., zeroes out frequency bands of the NMLS havingless than a threshold energy), as described in block 879 for instance.Further, AEC 554 may apply a frequency-dependent regularizationparameter to adapt an NMLS learning rate of change between AECiterations according to a magnitude of the measured signal, as describedin block 878 of FIG. 8A.

Given such features, the AEC may convert the sparse NMLS of the n^(th)frame of the error signal to the n^(th) update filter. Such conversionmay involve converting the sparse NMLS of the n^(th) frame to a matrixof filter coefficients and cross-band filtering the matrix of filtercoefficients to generate the n^(th) update filter, as described withrespect to block 878 of FIG. 8A.

g. Send Output Signal as Voice Input to Voice Service(s) for Processing

At block 914, the implementation 900 is configured to send the outputsignal as a voice input to one or more voice services for processing ofthe voice input. In some embodiments, the implementation 900 processesthe output signal as a voice input as described with respect to FIGS. 5Aand 5B. Such processing may involve detecting one or more wake words andone or more utterances. Further, such processing may involvevoice-to-speech conversion of the voice utterances, and transmitting thevoice utterances to a voice assistant services with a respect to processthe utterance as a voice input. Such transmitting may occur via anetwork interface, such as network interface 230.

V. Conclusion

The description above discloses, among other things, various examplesystems, methods, apparatus, and articles of manufacture including,among other components, firmware and/or software executed on hardware.In one embodiment, for example, a playback device (playback device 102)and/or a network microphone device (network microphone device 103) isconfigured to perform acoustic echo cancellation in an acousticenvironment (e.g., via implementation 900). It is understood that suchexamples are merely illustrative and should not be considered aslimiting. For example, it is contemplated that any or all of thefirmware, hardware, and/or software aspects or components can beembodied exclusively in hardware, exclusively in software, exclusivelyin firmware, or in any combination of hardware, software, and/orfirmware. Accordingly, the examples provided are not the only way(s) toimplement such systems, methods, apparatus, and/or articles ofmanufacture.

(Feature 1) A method to be performed by a system, the method comprisingcausing, via an audio stage, the one or more speakers to play back audiocontent; while audio content is playing back via the one or morespeakers, capturing, via the one or more microphones, audio within anacoustic environment, wherein the captured audio comprises audio signalsrepresenting sound produced by the one or more speakers in playing backthe audio content; receiving an output signal from the audio stagerepresenting the audio content being played back by the one or morespeakers; determining a measured signal comprising a series of framesrepresenting the captured audio within the acoustic environment bytransforming into a short time Fourier transform (STFT) domain thecaptured audio within the acoustic environment; determining a referencesignal comprising a series of frames representing the audio contentbeing played back via the one or more speakers by transforming into theSTFT domain the received output signal from the audio stage; during eachn^(th) iteration of an acoustic echo canceller (AEC): determining ann^(th) frame of an output signal, wherein determining the n^(th) frameof the output signal comprises: generating an n^(th) frame of a modelsignal by passing an n^(th) frame of the reference signal through ann^(th) instance of an adaptive filter, wherein the first instance of theadaptive filter is an initial filter; and generating the n^(th) frame ofthe output signal by redacting the n^(th) frame of the model signal froman n^(th) frame of the measured signal; determining a n+1^(th) instanceof the adaptive filter for a next iteration of the AEC, whereindetermining the n+1^(th) instance of the adaptive filter for the nextiteration of the AEC comprises: determining an n^(th) frame of an errorsignal, the n^(th) frame of the error signal representing a differencebetween the n^(th) frame of the model signal and the n^(th) frame of thereference signal less audio signals representing sound from sourcesother than an n^(th) frame of the audio signals representing soundproduced by the one or more speakers in playing back the n^(th) frame ofthe reference signal; determining a normalized least mean square (NMLS)of the n^(th) frame of the error signal; determining a sparse NMLS ofthe n^(th) frame of the error signal by applying to the NMLS of then^(th) frame of the error signal, a sparse partition criterion thatzeroes out frequency bands of the NMLS having less than a thresholdenergy; converting the sparse NMLS of the n^(th) frame of the errorsignal to an n^(th) update filter; and generating the n+1^(th) instanceof the adaptive filter for the next iteration of the AEC by summing then^(th) instance of the adaptive filter with the n^(th) update filter;and sending the output signal as a voice input to one or more voiceservices for processing of the voice input.

(Feature 2) The method of feature 1, further comprising beforedetermining the NMLS of the n^(th) frame of the error signal, applyingan error recovery non-linearity function to the error signal to limitthe error signal to a threshold magnitude, wherein determining thenormalized least mean square (NMLS) of the n^(th) frame of the errorsignal comprises determining the NMLS of the nth frame of the limitederror signal.

(Feature 3) The method of feature 2, wherein the error recoverynon-linearity function comprises a non-linear clipping function thatlimits portions of the error signal that are above the thresholdmagnitude to the threshold magnitude.

(Feature 4) The method of feature 1, wherein determining the normalizedleast mean square (NMLS) of the n^(th) frame of the error signalcomprises: applying a frequency-dependent regularization parameter toadapt an NMLS learning rate of change between AEC iterations accordingto a magnitude of the measured signal.

(Feature 5) The method of feature 1, wherein converting the sparse NMLSof the n^(th) frame of the error signal to the n^(th) update filtercomprises: converting the sparse NMLS of the n^(th) frame to a matrix offilter coefficients; and cross-band filtering the matrix of filtercoefficients to generate the n^(th) update filter.

(Feature 6) The method of feature 1, wherein the system excludes adouble-talk detector that disables the AEC when a double-talk conditionis detected, wherein capturing audio within the acoustic environmentcomprises capturing audio signals representing sound produced by two ormore voices.

(Feature 7) The method of feature 1, wherein the system comprises aplayback device comprising a first network interface and the one or morespeakers; and a networked-microphone device comprising a second networkinterface, the one or more microphones, the one or more processors, andthe data storage storing instructions executable by the one or moreprocessors, wherein the first network interface and the second networkinterface are configured to communicatively couple the playback deviceand the networked-microphone device.

(Feature 8) The method of feature 1, wherein the system comprises aplayback device comprising a housing configured to house the one or morespeakers and the one or more microphones.

(Feature 9) A tangible, non-transitory computer-readable medium havingstored therein instructions executable by one or more processors tocause a device to perform the method of any of features 1-8.

(Feature 10) A device configured to perform the method of any offeatures 1-8.

(Feature 11) A media playback system configured to perform the method ofany of features 1-8.

The specification is presented largely in terms of illustrativeenvironments, systems, procedures, steps, logic blocks, processing, andother symbolic representations that directly or indirectly resemble theoperations of data processing devices coupled to networks. These processdescriptions and representations are typically used by those skilled inthe art to most effectively convey the substance of their work to othersskilled in the art. Numerous specific details are set forth to provide athorough understanding of the present disclosure. However, it isunderstood to those skilled in the art that certain embodiments of thepresent disclosure can be practiced without certain, specific details.In other instances, well known methods, procedures, components, andcircuitry have not been described in detail to avoid unnecessarilyobscuring aspects of the embodiments. Accordingly, the scope of thepresent disclosure is defined by the appended claims rather than theforgoing description of embodiments.

When any of the appended claims are read to cover a purely softwareand/or firmware implementation, at least one of the elements in at leastone example is hereby expressly defined to include a tangible,non-transitory medium such as a memory, DVD, CD, Blu-ray, and so on,storing the software and/or firmware.

I claim:
 1. A system comprising: an audio stage comprising an audioprocessor and an audio amplifier; one or more speakers; one or moremicrophones; one or more processors; data storage storing instructionsexecutable by the one or more processors that cause the system toperform functions comprising: while audio content is playing back viathe one or more speakers, capturing, via the one or more microphones,audio within an acoustic environment, wherein the captured audiocomprises audio signals representing sound produced by the one or morespeakers in playing back the audio content; receiving a playback signalfrom the audio stage representing the audio content being played back bythe one or more speakers; transforming into a short time Fouriertransform (STFT) domain the captured audio within the acousticenvironment to generate a measured signal representing actual acousticecho; transforming into the STFT domain the received playback signalfrom the audio stage to generate a reference signal; during each n^(th)iteration of an acoustic echo canceller (AEC): determining an n^(th)frame of an output signal, wherein determining the n^(th) frame of theoutput signal comprises: generating an n^(th) frame of a model signalrepresenting estimated acoustic echo by passing an n^(th) frame of thereference signal through an n^(th) instance of an adaptive filter; andgenerating the n^(th) frame of the output signal by differencing then^(th) frame of the model signal and an n^(th) frame of the measuredsignal; determining a n+1^(th) instance of the adaptive filter for anext iteration of the AEC, wherein determining the n+1^(th) instance ofthe adaptive filter for the next iteration of the AEC comprises:estimating an n^(th) frame of an error signal, the n^(th) frame of theerror signal representing a difference between the n^(th) frame of themeasured signal and the n^(th) frame of the model signal; converting then^(th) frame of an error signal to an n^(th) update filter; deactivatinginactive portions of the n^(th) update filter, the inactive portionshaving less than a threshold energy; generating the n+1^(th) instance ofthe adaptive filter for the next iteration of the AEC by summing then^(th) instance of the adaptive filter with the n^(th) update filter;and sending the output signal as a voice input to one or more voiceservices for processing of the voice input.
 2. The system of claim 1,wherein converting the n^(th) frame of an error signal to the n^(th)update filter comprises determining a normalized least mean square(NMLS) of the n^(th) frame of the error signal, and wherein deactivatinginactive portions of the n^(th) update filter comprises determining asparse NMLS of the n^(th) frame of the error signal by applying to theNMLS of the n^(th) frame of the error signal, a sparse partitioncriterion that zeroes out frequency bands of the NMLS having less thanthe threshold energy.
 3. The system of claim 2, wherein the data storagefurther includes instructions that cause the system to perform functionscomprising: before determining the NMLS of the n^(th) frame of the errorsignal, applying an error recovery non-linearity function to the errorsignal to limit the error signal to a threshold magnitude, whereindetermining the normalized least mean square (NMLS) of the n^(th) frameof the error signal comprises determining the NMLS of the n^(th) frameof the limited error signal.
 4. The system of claim 3, wherein the errorrecovery non-linearity function comprises a non-linear clipping functionthat limits portions of the error signal that are above the thresholdmagnitude to the threshold magnitude.
 5. The system of claim 2, whereindetermining the normalized least mean square (NMLS) of the n^(th) frameof the error signal comprises: applying a frequency-dependentregularization parameter to adapt an NMLS learning rate of changebetween AEC iterations according to a magnitude of the measured signal.6. The system of claim 2, wherein converting the sparse NMLS of then^(th) frame of the error signal to the n^(th) update filter comprises:converting the sparse NMLS of the n^(th) frame to a matrix of filtercoefficients; and cross-band filtering the matrix of filter coefficientsto generate the n^(th) update filter.
 7. The system of claim 1,excluding a double-talk detector that disables the AEC when adouble-talk condition is detected, wherein capturing audio within theacoustic environment comprises capturing audio signals representingsound produced by two or more voices.
 8. The system of claim 1, furthercomprising: a playback device comprising a first network interface andthe one or more speakers; and a networked-microphone device comprising asecond network interface, the one or more microphones, the one or moreprocessors, and the data storage storing instructions executable by theone or more processors, wherein the first network interface and thesecond network interface are configured to communicatively couple theplayback device and the networked-microphone device.
 9. The system ofclaim 1, further comprising: a playback device comprising a housingconfigured to house the one or more speakers and the one or moremicrophones.
 10. A method to be performed by a system comprising aplayback device, the method comprising: while audio content is playingback via one or more speakers of the playback device, capturing, via oneor more microphones, audio within an acoustic environment, wherein thecaptured audio comprises audio signals representing sound produced bythe one or more speakers in playing back the audio content; receiving aplayback signal from an audio stage of the playback device, the playbacksignal representing the audio content being played back by the one ormore speakers; transforming into a short time Fourier transform (STFT)domain the captured audio within the acoustic environment to generate ameasured signal representing actual acoustic echo; transforming into theSTFT domain the received playback signal from the audio stage togenerate a reference signal; during each n^(th) iteration of an acousticecho canceller (AEC): determining an n^(th) frame of an output signal,wherein determining the n^(th) frame of the output signal comprises:generating an n^(th) frame of a model signal representing estimatedacoustic echo by passing an n^(th) frame of the reference signal throughan n^(th) instance of an adaptive filter; and generating the n^(th)frame of the output signal by differencing the n^(th) frame of the modelsignal and an n^(th) frame of the measured signal; determining an+1^(th) instance of the adaptive filter for a next iteration of theAEC, wherein determining the n+1^(th) instance of the adaptive filterfor the next iteration of the AEC comprises: estimating an n^(th) frameof an error signal, the n^(th) frame of the error signal representing adifference between the n^(th) frame of the measured signal and then^(th) frame of the model signal; converting the n^(th) frame of anerror signal to an n^(th) update filter; deactivating inactive portionsof the n^(th) update filter, the inactive portions having less than athreshold energy; generating the n+1^(th) instance of the adaptivefilter for the next iteration of the AEC by summing the n^(th) instanceof the adaptive filter with the n^(th) update filter; and sending theoutput signal as a voice input to one or more voice services forprocessing of the voice input.
 11. The method of claim 10, whereinconverting the n^(th) frame of an error signal to the n^(th) updatefilter comprises determining a normalized least mean square (NMLS) ofthe n^(th) frame of the error signal, and wherein deactivating portionsof the n^(th) update filter comprises determining a sparse NMLS of then^(th) frame of the error signal by applying to the NMLS of the n^(th)frame of the error signal, a sparse partition criterion that zeroes outfrequency bands of the NMLS having less than the threshold energy. 12.The method of claim 11, further comprising: before determining the NMLSof the n^(th) frame of the error signal, applying an error recoverynon-linearity function to the error signal to limit the error signal toa threshold magnitude, wherein determining the normalized least meansquare (NMLS) of the n^(th) frame of the error signal comprisesdetermining the NMLS of the n^(th) frame of the limited error signal.13. The method of claim 12, wherein the error recovery non-linearityfunction comprises a non-linear clipping function that limits portionsof the error signal that are above the threshold magnitude to thethreshold magnitude.
 14. The method of claim 11, wherein determining thenormalized least mean square (NMLS) of the n^(th) frame of the errorsignal comprises: applying a frequency-dependent regularizationparameter to adapt an NMLS learning rate of change between AECiterations according to a magnitude of the measured signal.
 15. Themethod of claim 11, wherein converting the sparse NMLS of the n^(th)frame of the error signal to the n^(th) update filter comprises:converting the sparse NMLS of the n^(th) frame to a matrix of filtercoefficients; and cross-band filtering the matrix of filter coefficientsto generate the n^(th) update filter.
 16. A tangible, non-transitory,computer-readable media having stored therein instructions executable byone or more processors to cause a system to perform functionscomprising: while audio content is playing back via one or more speakersof a playback device, capturing, via one or more microphones, audiowithin an acoustic environment, wherein the captured audio comprisesaudio signals representing sound produced by the one or more speakers inplaying back the audio content; receiving a playback signal from anaudio stage of the playback device, the playback signal representing theaudio content being played back by the one or more speakers;transforming into a short time Fourier transform (STFT) domain thecaptured audio within the acoustic environment to generate a measuredsignal representing actual acoustic echo; transforming into the STFTdomain the received playback signal from the audio stage to generate areference signal; during each n^(th) iteration of an acoustic echocanceller (AEC): determining an n^(th) frame of an output signal,wherein determining the n^(th) frame of the output signal comprises:generating an n^(th) frame of a model signal representing estimatedacoustic echo by passing an n^(th) frame of the reference signal throughan n^(th) instance of an adaptive filter; and generating the n^(th)frame of the output signal by differencing the n^(th) frame of the modelsignal and an n^(th) frame of the measured signal; determining an+1^(th) instance of the adaptive filter for a next iteration of theAEC, wherein determining the n+1^(th) instance of the adaptive filterfor the next iteration of the AEC comprises: estimating an n^(th) frameof an error signal, the n^(th) frame of the error signal representing adifference between the n^(th) frame of the measured signal and then^(th) frame of the model signal; converting the n^(th) frame of anerror signal to an n^(th) update filter; deactivating inactive portionsof the n^(th) update filter, the inactive portions having less than athreshold energy; generating the n+1^(th) instance of the adaptivefilter for the next iteration of the AEC by summing the n^(th) instanceof the adaptive filter with the n^(th) update filter; and sending theoutput signal as a voice input to one or more voice services forprocessing of the voice input.
 17. The tangible, non-transitory,computer-readable media of claim 16, wherein converting the n^(th) frameof an error signal to the n^(th) update filter comprises determining anormalized least mean square (NMLS) of the n^(th) frame of the errorsignal, and wherein deactivating portions of the n^(th) update filterhaving less than the threshold energy comprises determining a sparseNMLS of the n^(th) frame of the error signal by applying to the NMLS ofthe n^(th) frame of the error signal, a sparse partition criterion thatzeroes out frequency bands of the NMLS having less than the thresholdenergy.
 18. The tangible, non-transitory, computer-readable media ofclaim 17, further comprising: before determining the NMLS of the n^(th)frame of the error signal, applying an error recovery non-linearityfunction to the error signal to limit the error signal to a thresholdmagnitude, wherein determining the normalized least mean square (NMLS)of the n^(th) frame of the error signal comprises determining the NMLSof the n^(th) frame of the limited error signal.
 19. The tangible,non-transitory, computer-readable media of claim 18, wherein the errorrecovery non-linearity function comprises a non-linear clipping functionthat limits portions of the error signal that are above the thresholdmagnitude to the threshold magnitude.
 20. The tangible, non-transitory,computer-readable media of claim 17, wherein determining the normalizedleast mean square (NMLS) of the n^(th) frame of the error signalcomprises: applying a frequency-dependent regularization parameter toadapt an NMLS learning rate of change between AEC iterations accordingto a magnitude of the measured signal.